Bibliographic record
Abstract
Since the release of the documentary Blackfish in 2013, millions around the world have focused on the plight of the orca, the most profitable and controversial display animal in history. Yet, until now, no historical account has explained how we came to care about killer whales in the first place. Drawing on interviews, official records, private archives, and his own family history, Jason M. Colby tells the exhilarating and often heartbreaking story of how people came to love the ocean's greatest predator. Historically reviled as dangerous pests, killer whales were dying by the hundreds, even thousands, by the 1950s--the victims of whalers, fishermen, and even the US military. In the Pacific Northwest, fishermen shot them, scientists harpooned them, and the Canadian government mounted a machine gun to eliminate them. But that all changed in 1965, when Seattle entrepreneur Ted Griffin became the first person to swim and perform with a captive killer whale. The show proved wildly popular, and he began capturing and selling others, including Sea World's first Shamu. Over the following decade, live display transformed views of Orcinus orca. The public embraced killer whales as charismatic and friendly, while scientists enjoyed their first access to live orcas. In the Pacific Northwest, these captive encounters reshaped regional values and helped drive environmental activism, including Greenpeace's anti-whaling campaigns. Yet even as Northwesterners taught the world to love whales, they came to oppose their captivity and to fight for the freedom of a marine predator that had become a regional icon. This is the definitive history of how the feared and despised "killer" became the beloved "orca"--and what that has meant for our relationship with the ocean and its creatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.738 | 0.547 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".